The urban rail transit (URT) system in China has undergone development spanning over 50 years. In the period from 2008 to 2015, numerous URT lines were under construction. After 2015, an increasing number of cities have transitioned to multi-line network operations, with greater emphasis on system efficiency and passenger service. This transition has been accompanied by numerous successful innovations and applications aimed at enhancing system intelligence and automation. This paper provides a review of operational statistics based on annual reports, successful operational practices, and industry development characteristics over the past decade in mainland China. Additionally, suggestions and trends for the further development of URT in China are proposed.

The advancement of virtual coupling technology in urban rail transit has facilitated the online coupling and decoupling of trains, enabling a range of flexible transportation configurations, including various route types and adjustable formations. This study targets the fluctuating passenger demands on urban rail lines, aiming to minimize both passenger travel and operational costs. The model integrates constraints associated with virtual coupling, train operations, rolling stock circulation, and the interaction between virtually coupled trains and passenger arrivals. New decision variables are introduced to depict the train formation state under virtual coupling scenarios. An integrated optimization model for train diagrams and rolling stock circulation under virtual coupling conditions is developed, employing a genetic-simulated annealing algorithm informed by train operation simulations. A case study on an urban rail line during the morning peak examines the optimization of train diagrams for full-length and short-turn routes. Findings confirm that virtual coupling technology effectively adapts to lines with uneven passenger flow distribution, significantly enhancing the match between supply and demand, equalizing spatial and temporal traffic variations, and harmonizing the quality of passenger services with operational efficiency.

Rail operators in many countries discount group tickets to improve revenue by increasing price-driven demand. For individual passengers, dynamic pricing is beneficial for maximizing revenue based on the price discrimination principle. Usually, group fares are cheaper than individual fares. If too many group tickets are sold, there will not be enough tickets available to meet high-priced individual demand; by contrast, if not enough group tickets are sold and there is insufficient individual demand, the unsold seats will not have value once the train departs. Therefore, for railway operators, it is worth looking for a balance between group discounts and dynamic pricing to maximize benefits. Essentially, rail operators need to find the symmetry point of the expected revenue between accepting group bookings and reserving tickets for individuals when making decisions. In this study, we formulated a joint decision model of group ticket booking control and dynamic pricing and investigated the effect of the joint decision. The results of numerical experiments showed that incorporating group discounts into dynamic pricing can improve expected revenue when passenger demand is weak, and compared to setting fixed quantities for group tickets, dynamically controlling the limit of group bookings can effectively increase expected revenue. Further analysis of the impacts of time, number of tickets sold, and group demand was helpful to implement the proposed joint policy.

Train delays have a great impact on the schedule of high-speed railways including overall efficiency and the quality of the travel service of the passengers. Therefore, the development of an approach to recover a schedule via timely and rapid operation adjustments must be investigated. In this paper, a complete final real-time adjustment scheme is proposed for the train dispatcher of a railway enterprise for delayed trains. A train operation process model based on the Max-Plus algebra method is proposed. Six operation adjustment strategies are analyzed including section acceleration, operation based on the original timetable, dwell time reduction, increase in overtaking, reduction in overtaking, and train postponement. An approximate model is then built based on the minimum number of delayed trains considering the constraints of the adjustment strategies and the feasible adjusted schemes can be quickly obtained without any record specific time data and constraints. The set of feasible solutions of the approximate model is then regarded as the importation of the second model. In addition, the second model is an optimization model for operation adjustment with the least average delay time of each train at each station by updating the state matrixes of the train operation process model. The algorithms are designed for these models, and this approach can reduce the computation time. Finally, the timetable of the Beijing-Shanghai high-speed railway is considered as the actual case for analysis. Thus, the method was proven feasible for operation adjustment of delayed trains.